Again, note that the original Metaview contains some
=20's and a few =9's, which I have removed. And again,
readers are invited to check for themselves if I have
correctly conveyed the original message by doing so.

BG> Below is another posting from William Dembski at
BG> Baylor University in Waco, TX. Dembski
BG> continues his discussion of evolutionary
BG> algorithms (see Meta 139) and presents a
BG> mathematical argument for why such algorithms
BG> cannot generate specified complexity

Dembski does no such thing. The points of critique
launched in this essay are limited to:

1) Evolutionary algorithms always solve their
problems, setting the probability of success at 1, and
the complexity therefore at 0.

2) Evolutionary algorithms get their "specified
complexity" from the fitness functions, and thus have
not *created* it.

None of these are supported by any kind of
"mathematics", unless one considers any essay with
numbers in it to be "mathematical".

BG> as asserted by Richard Dawkins.

Dembski here continues the practice I also critiqued
in my previous installment of ID-Commentary: Namely,
to only criticize Dawkins' "misleading" Weasel
program, instead of dealing with *real* problems
solved by evolutionary algorithms, as asked by critics
of Dembski. This is especially suspect, since "Why
Evolutionary Algorithms Cannot Generate Specified
Complexity" (and its companion-piece "Explaining
Specified Complexity") is being presented as an *in
principle*-refutation of the possibility of
evolutionary algorithms producing "specified
complexity".

When speaking to the general public, who only know
Dawkins' Weasel program, this tactic might work very
well, but leave more informed skeptics wondering why
Dembski keeps avoiding the *real* challenges, if his
"explanatory filter" really is capable of doing what
has been attributed to it.

BG> A number of equations are presented in the
BG> appendix.
BG>
BG> Dembski concludes that "all the specified
BG> complexity we get out of an evolutionary algorithm
BG> has first to be put into the construction of the
BG> evolutionary algorithm and into the fitness
BG> function that guides the algorithm. Evolutionary
BG> algorithms therefore do not generate or create
BG> specified complexity, but merely harness already
BG> existing specified complexity." I am not sure I
BG> follow the entire argument,

Bill Grassie's confusion is understandable, since
Dembski has a wonderful ability to cloak everything he
says in a highly techincal and intimidating babble.
Therefore, most of my comments will deal with what
Dembski is actually *saying*, trying to "translate"
his impressive-sounding lingo, showing that it often
covers simple and uncontroversial statements.

BG> but I am certainly reminded of my first
BG> programming course as a freshman in college in
BG> 1975, when I clocked 70 hours one week in the lab
BG> trying to code a quick sort algorithm. Some more
BG> teleological interventions would have helped.
BG>
BG> I will entertain responses on the Metaviews list
BG> and try to run some compilation in a week or so.
BG> If you want immediate gratification conversation,
BG> check out the Reiterations List at for a higher
BG> volume, lightly moderated discussion.
BG>
BG> -- Billy Grassie
BG>
WAD> =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
WAD> =-=-= From: bill@desiderius.com (William A.
WAD> Dembski) Subject: Specified Complexity
WAD>
WAD> WHY EVOLUTIONARY ALGORITHMS CANNOT GENERATE
WAD> SPECIFIED COMPLEXITY by William A. Dembski
WAD>
WAD> In my last piece for META, I asserted that
WAD> evolutionary algorithms cannot generate specified
WAD> complexity and motivated this assertion by
WAD> pointing to the failure of Richard Dawkins's well-
WAD> known METHINKS IT IS LIKE A WEASEL example to
WAD> generate specified complexity. My point was that
WAD> Dawkins's evolutionary algorithm converged on
WAD> METHINKS IT IS LIKE A WEASEL with probability
WAD> one, and therefore reduced the complexity of
WAD> generating this sequence to zero. With reference
WAD> to specified complexity, complexity and
WAD> probability are inverse notions: High complexity
WAD> presupposes many live possibilities and
WAD> correspondingly assigns low probability to anyone
WAD> of these possibilities. Thus, while it's true
WAD> that shaking out random scrabble pieces would
WAD> render METHINKS IT IS LIKE A WEASEL highly
WAD> improbable (and therefore complex), Dawkins's
WAD> evolutionary algorithm renders that sequence
WAD> certain and thereby removes its complexity.
WAD>
WAD> Basically, the problem here is one of setting the
WAD> relevant probabilistic context. Within a random-
WAD> scrabble-shaking-scenario, this sequence is
WAD> complex and specified, but within Dawkins's
WAD> evolutionary algorithm it is no longer complex
WAD> (though it remains specified). I therefore
WAD> concluded my last piece by saying that just as
WAD> Darwinian evolution only delivers the
WAD> **appearance** of design (an assertion all
WAD> Darwinists perforce accept), so too it only
WAD> delivers the **appearance** of specified
WAD> complexity.

Dembski forgets the other half of his conlusion: That
his actual/appearant split of "specified complexity"
makes it considerably more difficult to determine
whether life indeed *is* an instance of specified
complexity:

[...]
WAD> In general terms, the problem of generating
WAD> specified complexity via an evolutionary
WAD> algorithm can be conceived as follows. We are
WAD> given a phase space of possible solutions to a
WAD> problem and a fitness function over that phase
WAD> space. Our task is to optimize this fitness
WAD> function by finding a point in the phase space
WAD> that attains a certain level of fitness. Think of
WAD> it this way: The phase space is a vast plane, the
WAD> fitness function is a vast hollowed-out mountain-
WAD> range over the plane (complete with low-lying
WAD> foothills and incredibly high peaks). The task of
WAD> an evolutionary algorithm is by moving around in
WAD> the plane to get to some point under the mountain-
WAD> range where it attains at least a certain height
WAD> (e.g., 10,000 feet). The collection of all such
WAD> places on the plane where the mountain range
WAD> attains at least that height (here 10,000 feet)
WAD> we will call the **target**. Thus the job of the
WAD> evolutionary algorithm is by navigating the phase
WAD> space to find its way into the target (see
WAD> Appendix 1 below).

What Dembski here calls the "phase space" is already
known to readers of Dawkins as "genetic space":

"Imagine a museum with gallaries stretching
towards the horizon in every direction, and as
far as the eye can see upwards and downwards
as well. Preserved in the museum is every kind
of animal form that has ever existed, and
every kind that could be imagined. Each animal
is housed next door to those it most
resembles. Each dimension in the museum -that
is, each dimension along which a gallary
extends- corresponds to one dimension in which
the animals vary. For example, as you walk
north along a particular gallary you notice a
progressive lengthening of the horns of the
speciments in the cabinets. Turn round and
walk south and the horns shorten. Turn and
walk east and that horns stay the same but
something else changes, say the teeth get
sharper. Walk west and the teeth grow blunter.
Since horn length and teeth sharpness are only
two out of thousands of ways in which animals
can vary, the gallaries must criss-cross one
another in many-dimensional space, not just
the ordinary three-dimensional space that we,
with our limited minds, are capable of
visualizing." (Dawkins, R., 1996, "Climbing
Mount Improbable", pp. 200)

In the case of Dawkins' weasel program, the "phase
space" is 28-dimensional (since there are 28 positions
that can vary), where each dimension is 27 characters
long (since there are 26 letters + space). In the
weasel program, the algorithm can move any numbers of
characters, but is restricted to moving a certain
number of dimensions at a time (kinda' like the tower
in chess, which can only move either back-forth or
left-right, but can move any number of spaces).

WAD> Now, the phase space (which we are picturing as a
WAD> giant plane) usually comes with some additional
WAD> topological structure, typically given by a
WAD> metric or distance function (see Appendix 2).
WAD> This topological structure tells us how points in
WAD> the phase space are related geometrically to
WAD> nearby points.

The concept Dembski is trying to convey is that known
to the biological community as a "fitness landscape",
where increasing altitude stands for increasing
fitness, as defined in terms of reproductive sucess.

In the case of Dawkins' weasel program, the fitness
landscape is a 29-dimensional cone, placed "over" the
28-dimensional "chessboard" (a.k.a. "phase space"). On
the space directly under the top of the cone, the
target sequence ("methinks it is like a weasel") is
written, while the spaces around it are labelled with
sequences very close to it (e.g. "yethinks it is like
a weasel" and "methinks it is like a geasel").

WAD> Also, even though the phase space is huge, it
WAD> tends to be finite (strictly finite for problems
WAD> represented on computer and topologically finite,
WAD> or what topologists call "compact," in general).

This is quite uncontroversial. The number of possible
28-letter sequences, each position with 27 possible
outcomes (28^27 ~ 10^39) *is* "huge, [but] finite".

WAD> Moreover, such spaces typically come with a
WAD> uniform probability that is adapted to the
WAD> topology of the phase space (see Appendix 3).

With respect to Dawkins' weasel program, this pretty
much means that the very first sequence has no more
probability coming up "jhdhonfybyyeev nzyvqqtiilke"
than "xgyhsnszciuhanomqtwlpgwaaumu". I know of no
algorithms, where this is not the case. Dembski's
reason for mentioning this is unclear.

WAD> Basically this means that if you get out your
WAD> tape measure and measure off a three by five foot
WAD> area in one part of the phase space, the uniform
WAD> probability will assign it the same probability
WAD> as a three by five foot area in another portion
WAD> of the phase space. All the spaces to which I've
WAD> seen evolutionary algorithms applied do indeed
WAD> satisfy these two conditions of having a finite
WAD> topological structure (i.e., they are compact)
WAD> and possessing a uniform probability. Moreover,
WAD> this uniform probability is what typically gets
WAD> used to estimate the complexity/improbability of
WAD> the target (i.e., the area of the phase space
WAD> under the mountain range where it attains a
WAD> certain requisite level -- e.g., 10,000 feet).
WAD>
WAD> For instance, in Dawkins's
WAD> METHINKS*IT*IS*LIKE*A*WEASEL example, the phase
WAD> space consists of strings of upper case Roman
WAD> letters and spaces (represented by asterisks) of
WAD> length 28. A uniform probability on this space
WAD> assigns equal probability to each of these
WAD> sequences -- approximately 1 in 10^40. It's this
WAD> improbability that corresponds to the complexity
WAD> of the target sequence and with respect to which
WAD> this target sequence constitutes an instance of
WAD> specified complexity.

Again, Dembski is being very unclear about what
*exactly* he means by "specified complexity". Judging
from the above, one would think that the "complexity"
(i.e. "propability") of a certain event should be
calculated only with respect to a single chance
hypothesis. But in TDI (pp. 50) Dembski says that his
explanatory filter needs to "sweep the field clear of"
*all* chance hypotheses.

This wouldn't be much of a problem, since "specified
complexity" is never even mentioned in TDI. But since
Dembski is constantly referring to specified
complexity as a characteristic feature of "design", as
well as to TDI as his "scholarly argument" for his
ideas, this is unlikely to be anything *but* a
problem.

WAD> In general, given a phase space with a target
WAD> sitting under those places where the mountain
WAD> range attains at least a certain level (e.g.,
WAD> 10,000 feet), the (uniform) probability of
WAD> randomly choosing a point from the phase space
WAD> and landing in the target will be very small. In
WAD> Dawkins's example, the target equals the
WAD> character string METHINKS*IT*IS*LIKE*A*WEASEL and
WAD> the improbability is 1 in 10^40. For non-toy
WAD> examples the improbability is typically much less
WAD> than my universal probability bound of 1 in
WAD> 10^150 that I justify in The Design Inference
WAD> (Cambridge, 1998; cf. section 6.5). Indeed, if
WAD> the probability of the target were not small, a
WAD> random search through the phase space would
WAD> suffice to find a point in the target, and there
WAD> would be no need to construct an evolutionary
WAD> algorithm to find it.

Again, few people would disagree that "methinks it is
like a weasel" is too long to find just by randomly
selecting letters and spaces. Indeed, Dawkins himself
concluded that it "would be a long time coming" before
this would produce the target sequence ("The Blind
Watchmaker", pp. 47).

WAD> We therefore suppose that the target is just a
WAD> tiny portion of the whole phase space; or, in
WAD> slightly more technical language, the (uniform)
WAD> probability of the target in relation to the
WAD> phase space as a whole is exceedingly small.
WAD> What's more, the target, in virtue of its
WAD> explicit identification, is specified (certainly
WAD> this is the case in Dawkins's example where the
WAD> target includes but one point and coincides with
WAD> the character string
WAD> METHINKS*IT*IS*LIKE*A*WEASEL). Thus it would seem
WAD> that to find a point in the target would be to
WAD> generate specified complexity.

But just as Morris and Whitcomb thinks that
radiometric datings only show the "appearent age" of
the Earth, so Dembski believes that the solution
produced is only "appearant specified complexity".

WAD> But let's look deeper. Consider an evolutionary
WAD> algorithm that does in fact find the target. An
WAD> evolutionary algorithm can be conceived as a
WAD> stochastic process that moves around the phase
WAD> space some finite number of times (see Appendix
WAD> 4). Let's call the evolutionary algorithm E. The
WAD> evolutionary algorithm starts at some point E(0)
WAD> in the phase space (usually chosen at random).
WAD> Then it moves to E(1). Then to E(2). Then to E
WAD> (3). Etc. For E successfully to find the target
WAD> (i.e., to find a point under the mountain range
WAD> where it attains at least a certain level --
WAD> e.g., 10,000 feet) then means that within a
WAD> manageable number of steps n, E is very likely to
WAD> land in the target -- i.e., some one of E(0), E
WAD> (1), ..., E(n) is likely to land in the target
WAD> (see Appendix 5). Simply put, the algorithm E has
WAD> to get us into the target with high probability
WAD> and in a relatively short number of steps. In the
WAD> Dawkins example, E(n) rapidly converged to
WAD> METHINKS*IT*IS*LIKE*A*WEASEL for n around 40.
WAD>
WAD> An evolutionary algorithm needs to be contrasted
WAD> with pure random sampling. Pure random sampling
WAD> treats the phase space as a giant urn from which
WAD> we draw items at random according to the uniform
WAD> probability. In that case, a random sample from M
WAD> of size k will contain a point in the target with
WAD> probability better than 1/2 provided that k is
WAD> around the reciprocal of the (uniform)
WAD> probability of the target. Since we are assuming
WAD> that the probability of the target is less than
WAD> my universal probability bound of 1 in 10^150
WAD> given earlier, it follows that k will need to be
WAD> at least 10^150. This number is enormous and far
WAD> exceeds the number of computations conceivable
WAD> for any traditional computer. Moreover, it
WAD> doesn't seem that quantum computation is going to
WAD> render this number tractable either since the
WAD> points in phase space need to be made explicit in
WAD> any random sampling scheme (implying decoherence
WAD> and thus preventing us from exploiting quantum
WAD> superposition).

Since all of the above is the case, both with respect
to Dawkins' weasel program, as well as all examples of
evolutionary algorithms that I am aware of, I am
puzzled as to why Dembski finds it relevant to
mention.

WAD> Let's now return to the evolutionary algorithm E.
WAD> We're going to allow ourselves a certain number
WAD> of steps, call it m, for E to land in the target.
WAD> Clearly m is going to have to be much less than
WAD> 10^150 if we're going to program E on a computer
WAD> and have any hope of E landing in the target.
WAD> With m fixed, we can determine the probability
WAD> that E will land in any subset of phase space in
WAD> m steps (see Appendix 6). For instance, in the
WAD> Dawkins example, for m = 100 and the target
WAD> sequence METHINKS*IT*IS*LIKE*A*WEASEL and E the
WAD> cumulative selection algorithm Dawkins
WAD> constructed, the probability of E attaining the
WAD> target in m = 100 steps is approximately 1.
WAD>
WAD> What this means is that even though with respect
WAD> to the uniform probability on the phase space the
WAD> target has exceedingly small probability, the
WAD> probability for the evolutionary algorithm E to
WAD> get into the target in m steps is no longer
WAD> small. And since complexity and improbability are
WAD> for the purposes of specified complexity parallel
WAD> notions, this means that even though the target
WAD> is complex and specified with respect to the
WAD> uniform probability on the phase space, it
WAD> remains specified but is no longer complex with
WAD> respect to the probability induced by
WAD> evolutionary algorithm E.

Now Demsbki seems to have returned to claiming that
complexity needs to be calculated with regard to *all*
relevant chance hypotheses (as opposed to just the
"uniform probability").

While few would disagree that life is complex with
regard to the chance hypothesis of it being assembled
by throwing random molecules together, it is quite
another matter if it is complex with regard to it
having come about through the actualization of
heritable modifications, exclusion of certain
modifications through differental reproductive
success, and specified through the conditions of the
environment (i.e. natural selection). In fact, whether
this is so is the very point in question, and IDers
are just assuming their conclusion when they claim
that life contains "specified complexity".

WAD> Does this mean that the evolutionary algorithm
WAD> has in fact generated complex specified
WAD> information, but that in referring to a loss of
WAD> complexity with respect to E I'm simply engaging
WAD> in some fancy redefinitions to avoid this
WAD> conclusion? I don't think so. Remember that we
WAD> are interested in the **generation** of specified
WAD> complexity and not in its reshuffling.

This seems to be a complete non sequitur. Dembski
hasn't shown that the "specified complextiy" has been
"reshuffled", and his "reminding us of it" seems
obscure. Indeed, Dembski doesn't even think that there
is any specified complexity to be "reshuffled" to
begin with! According to his argument, the sequence
produced by Dawkins' weasel program doesn't contain
specified complexity because it is produced by
Dawkins' weasel program.

And contrary to Dembski's assertions, he *is* "simply
engaging in some fancy redefinitions", if only with
respect to claims that life contains "specified
complexity".

WAD> To see what's at stake here, we need to be clear
WAD> about a restriction that needs to be placed on E
WAD> if it is to count as a genuine evolutionary
WAD> algorithm (i.e., a legitimate correlative of the
WAD> Darwinian mutation-selection mechanism). It is
WAD> not, for instance, legitimate for E to be able to
WAD> survey the mountain range (i.e., fitness
WAD> landscape), see where in the phase space it
WAD> attains a global maximum, and then head in that
WAD> direction. That would be teleology. No, E must be
WAD> able to navigate its way to the target either by
WAD> randomly choosing points from the phase space or
WAD> by using those as starting points and then
WAD> selecting other points in the phase space based
WAD> **solely** on the topology of the phase space and
WAD> without recourse to the fitness function, except
WAD> to evaluate the fitness function at individual
WAD> points of the phase space already traversed by E.
WAD> In other words, E must move around the phase
WAD> space only on the basis of its topology and the
WAD> elevation of the fitness function at points in
WAD> the phase space already traversed by E.

Of course not! Again, since this doesn't apply to any
of the evolutionary algorithms that Dembski is
supposed to deal with, I am at a loss, trying to
understand why Dembski considers it relevant to
mention.

[...]
WAD> Certainly this means that the evolutionary
WAD> algorithm E is highly constrained in the use it
WAD> can make of the fitness function. But there's
WAD> more. It means that the success of E in hitting
WAD> the target depends crucially on the structure of
WAD> the fitness function.

Finally, Dembski seems to have arrived at his major
criticism of evolutionary algorithms as producers of
specified complexity: They don't produce specified
complexity, but gets it from the fitness function.

WAD> If, for instance, the fitness function is totally
WAD> flat and close to the ground whenever it is
WAD> outside the target, then it fails to discriminate
WAD> between points outside the target and so cannot
WAD> be any help guiding an evolutionary algorithm
WAD> into the target. For such a fitness function, the
WAD> probability of the evolutionary algorithm landing
WAD> in the target is no better than the probability
WAD> of pure random sampling landing in the target,
WAD> which as we know is inadequate to get us there
WAD> (see Appendix 7).
WAD>
WAD> But the problem is even worse. It follows by a
WAD> combinatorial argument that for any partition of
WAD> the phase space into pieces none of which has
WAD> probability more than the probability of the
WAD> target (which by assumption is less than 1 in
WAD> 10^150), for the vast majority of these partition
WAD> elements the probability of the evolutionary
WAD> algorithm E entering them is going to be no
WAD> better than pure random sampling. It follows that
WAD> the vast majority of fitness functions on the
WAD> phase space that coincide with our original
WAD> fitness function on the target but reshuffle the
WAD> function on the partition elements outside the
WAD> target will not land the evolutionary algorithm
WAD> in the target (this result is essentially a
WAD> corollary of the No Free Lunch theorems by
WAD> Wolpert and Macready).

As I also pointed out, last week, Dembski's (mis)use
of Wolpert and Macready's "No Free Lunch theorems" is
bordering on the intellectually dishonest. According
to Wesley, "NFL isn't about essential capacity of an
algorithm to produce a solution; it is about
comparative efficiency of algorithms in producing
solutions." (see my last Commentary)

WAD> Simply put, the vast majority of fitness
WAD> functions will not guide E into the target even
WAD> if they coincide with our original fitness
WAD> function on the target (see Appendix 8).

In order to put Dembski's objection into perspective,
allow me to use a specific example: An evolutionary
biologist might claim that a certain rodent can evolve
longer teeth, if having longer teeth confers a
reproductive advantage: Mutations for longer teeth
appear and are selected for, increasing the specified
complexity of the genome of the offspring of the
rodent (if only with respect to the "uniform
probability").

Dembski's hypothetical response to this would be that,
Yes, natural selection indeed *can* enlarge the teeth
of rodents, thereby increasing the specified
complexity (with respect to the "uniform probability")
of its genome. But since it depends on longer teeth
confering a reproductive advantage, this specified
complexity hasn't really been created, only
"reshuffled".

The creative act of the Intelligent Designer would in
this case be to determine that having longer teeth
would cause the rodent in question to have more
offspring.

One wonders if this is the same Dembski who wrote that
"design ... located in natural laws ... becomes an
empty metaphor":

"But as soon as design is located in natural
laws, design becomes an empty metaphor. I know
what it is for a watch to be designed. I only
know what it is for the *process* of making a
watch to be designed in the derivative sense
that I know what it is for a watch to be
designed. Locating design in natural laws has
the effect of reversing this ordinary logic
and thereby vitiating design. If I can't
ascertain that a thing is designed, I can't
ascertain that that the process giving rise to
the thing is designed. Unless we can infer an
intelligent agent from the structure, dynamics
and function of *things*, we are not going to
infer such an agent from the *processes* that
agent supposedly used to bring about those
things. If imputing design to things is
problematic, then imputing design to the
processes that gave rise to those things is
doubly problematic." (Dembski, W.A.,
1999, "Intelligent Design: The Bridge Between
Science and Theology", pp. 78, original
emphasis)

This internal inconsistency on Dembski's part
notwithstanding, his objection suffers from serious
problems.

First of all, it is clear that the role of natural
selection in the production of specified complexity
still looms large enough to call into question the
sweeping claims made about Dembski's explanatory
filter having reinstated God within science. Even if
the objection of "Why Evolutionary Algorithms Cannot
Generate Specified Complexity" and the claim that life
contained specified complexity were to be taken at
face value, Dembski's explanatory filter would at most
allow for some sort of deist-god, creating the
universe with the physical constants and mechanis ms
that would make it possible for life to evolve by
purely natural processes. This might be what Dembski
means by "designed", but I doubt that the evangical
Christian community buying his books or in other ways
supporting the ID movement financially will like what
Dembski is saying.

Second, and more importantly, Dembski's objection is
difficult (if not impossible!) to test. At the moment,
we have no idea what causes the natural constants to
be the way they are, and thus, any hope of putting
fitness functions into Dembski's explanatory filter is
a far way into the future. And if those doesn't happen
to be designed either, Dembski can just claim that
whatever caused *them* to be that way must be
designed.

Indeed, there is a problem of infinite regress here.
Whenever the source of whatever feature in question is
offered, Dembski can just lean back and ask "Well, how
did *that* come about?" And since one must always
produce a new source to satisfy him, Dembski can
continue playing this game for as long as he wants (or
until his opponents grow tired of playing with him).

The third problem with Dembski's objection flows
naturally from the second. Earlier this year, Dembski
claimed that "[i]f it could be shown that biological
systems like the bacterial flagellum that are
wonderfully complex, elegant, and integrated could
have been formed by a gradual Darwinian process (which
by definition is non-telic), then intelligent design
would be falsified on the general grounds that one
doesn't invoke intelligent causes when purely natural
causes will do."
(<http://www.discovery.org/viewDB/index.php3?program=CRSC%20Responses&command=view&id=584>)

It could be argued that an unknown Designer, for
whatever reasons, created certain features with an
"appearant evolvability", and that this possible
falsification would only falsify the design of
biological structures anyway (leaving Ross' "fine
tuning argument" safe and sound).

But by assigning specified complexity to the fitness
landscape, Dembski has effectively destroyed any hope
of ID ever being falsifiable (at least with respect to
"wonderfully complex, elegant, and integrated"
"biological systems" being "formed by a gradual
Darwinian process").

Whenever natural selection (or, as in this case,
evolutionary algorithms) is observed producing any of
the things that, according to Dembski, was directly
and supernaturally designed, he can just claim that it
was all "hardwired" in the forces of nature.

[...]
WAD> I have omitted many details. I have also omitted
WAD> some complications which to my mind make the
WAD> problem of generating specified complexity via
WAD> evolutionary algorithms even more problematic (in
WAD> nature, for instance, the fitness function will
WAD> not stay fixed but vary over time).

Dembski needs to show why this is "problematic" (as
oppposed to "easy" or "indifferent").

WAD> Some of the details are treated in chapter 6 of
WAD> my recently released Intelligent Design: The
WAD> Bridge Between Science & Theology (InterVarsity).

As already reported, "Intelligent Design" contains
little, if any, new material. It merely repeats
Dembski's assertion that any information "created" by
any un-intelligent processes must be contained in the
process from the start.

WAD> A full treatment will have to await a book I'm
WAD> currently writing (Redesigning Science: Why
WAD> Specified Complexity Is a Reliable Empirical
WAD> Marker of Actual Design).

It will be interesting to see if *this* book contains
the "in principle refutation" that so far have been
lacking...

WAD> But I want to make these preliminary results
WAD> available because the misconception that one can
WAD> purchase specified complexity on the cheap is
WAD> widespread and ill-conceived.
WAD>
WAD> The only known generator of specified complexity
WAD> that we know is intelligence.

Given Dembski's stringent criteria for what can be
considered to be "specified complexity", it is
questionable if even the actions of intelligent
entities can be considered to be manifestations of
"specified complexity".

WAD> Sans intelligence, a process that yields
WAD> specified complexity merely converts already
WAD> existing specified complexity.
WAD> We are seeing a similar phenomenon with
WAD> inflationary cosmologies, which attempt to wash
WAD> out cosmological fine-tuning but invariably seem
WAD> to smuggle it back in. Smuggling in specified
WAD> complexity is not the same as **generating**
WAD> specified complexity. I challenge the biological
WAD> community to take these results seriously, and
WAD> reevaluate how it understands the generation of
WAD> specified complexity.
[...]
META> Permission is granted to reproduce this e-mail
META> and distribute it without restriction with the
META> inclusion of the following credit line: [This is
META> another posting from the Meta-List . Copyright
META> 1997, 1998, 1999. William Grassie.]
-------------------------------------------------------

=====
Morgan

"Evolution is to the social sciences as statues are to
birds: a convenient platform upon which to deposit badly
digested ideas." (Steve Jones, 2000, "Darwin's Ghost", pp.
xxvii)